Feature Extraction
Transformers
Safetensors
English
multilingual
laya_browser
laya
custom_code
system-1
browser-agent
web-navigation
decision-model
mmbert
mind2web
tilelang
Instructions to use cklxx/laya-browser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cklxx/laya-browser with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cklxx/laya-browser", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cklxx/laya-browser", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download code/finetune/resume_v17s.sh from cklxx/laya-browser: direct link, hf CLI and curl.
- Browser
- Download file 451 Bytes
-
https://huggingface.co/cklxx/laya-browser/resolve/main/code/finetune/resume_v17s.sh
- Command line
-
hf download hf://cklxx/laya-browser/code/finetune/resume_v17s.sh
-
curl -L -o resume_v17s.sh https://huggingface.co/cklxx/laya-browser/resolve/main/code/finetune/resume_v17s.sh
451 Bytes
| # After a reboot: continue (or start) the v17s run. train.py resumes from finetune/out/laya-browser-v17s/resume.pt if it | |
| # exists (written every 300 optimizer steps and at each epoch end); SKIP_BUILD reuses finetune/out/train_items.pt. | |
| cd /home/ckl/projects/S/laya | |
| nohup env SKIP_BUILD=1 bash finetune/run_v17s.sh 1 > finetune/out/v17s.log 2>&1 & | |
| echo "v17s running in the background (pid $!); follow with: tail -f finetune/out/v17s.log" | |